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A review of global potentially available cropland estimates and their consequences for model-based assessments

期刊

GLOBAL CHANGE BIOLOGY
卷 21, 期 3, 页码 1236-1248

出版社

WILEY-BLACKWELL
DOI: 10.1111/gcb.12733

关键词

agricultural intensification; cropland expansion; land availability; land reserve; land use; land-change model

资金

  1. EU
  2. European Research Council [311819]

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The world's population is growing and demand for food, feed, fiber, and fuel is increasing, placing greater demand on land and its resources for crop production. We review previously published estimates of global scale cropland availability, discuss the underlying assumptions that lead to differences between estimates, and illustrate the consequences of applying different estimates in model-based assessments of land-use change. The review estimates a range from 1552 to 5131Mha, which includes 1550Mha that is already cropland. Hence, the lowest estimates indicate that there is almost no room for cropland expansion, while the highest estimates indicate that cropland could potentially expand to over three times its current area. Differences can largely be attributed to institutional assumptions, i.e. which land covers/uses (e.g. forests or grasslands) are societally or governmentally allowed to convert to cropland, while there was little variation in biophysical assumptions. Estimates based on comparable assumptions showed a variation of up to 84%, which originated mainly from different underlying data sources. On the basis of this synthesis of the assumptions underlying these estimates, we constructed a high, a medium, and a low estimate of cropland availability that are representative of the range of estimates in the reviewed studies. We apply these estimates in a land-change model to illustrate the consequences on cropland expansion and intensification as well as deforestation. While uncertainty in cropland availability is hardly addressed in global land-use change assessments, the results indicate a large range of estimates with important consequences for model-based assessments.

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